<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Concrete is the most common and important building material nowadays. Its compressive strength plays a crucial role in the result and safety of the building. To improve the efficiency of concrete compressive strength detection, this study combines intelligent machine vision technology to design a concrete compressive strength detection system. The features of concrete are extracted using the edge detection method. Then the extracted features are classified using the random forest method to complete the identification and localization of concrete. Based on this basis, the compressive strength of concrete is calculated and detected based on the conversion relationship between uniaxial compressive strength and point load strength. Finally, after testing the performance of the system, the practical effects of the system are examined. According to the results, the system’s detection rate is between 0.058 and 0.072 seconds, and the recognition accuracy and classification accuracy of the four different types of concrete detection exceed 80%. The relative error values for the detected compressive strength were 5.87% and 3.52%, respectively, and they passed the compressive strength detection of retardation diagrams in complex situations. The excellent performance of this study in real concrete detection meets the demand for concrete compressive detection in reality.</p>
</abstract>

Concrete is the most common and important building material nowadays. Its compressive strength plays a crucial role in the result and safety of the building. To improve the efficiency of concrete compressive strength detection, this study combines intelligent machine vision technology to design a concrete compressive strength detection system. The features of concrete are extracted using the edge detection method. Then the extracted features are classified using the random forest method to complete the identification and localization of concrete. Based on this basis, the compressive strength of concrete is calculated and detected based on the conversion relationship between uniaxial compressive strength and point load strength. Finally, after testing the performance of the system, the practical effects of the system are examined. According to the results, the system’s detection rate is between 0.058 and 0.072 seconds, and the recognition accuracy and classification accuracy of the four different types of concrete detection exceed 80%. The relative error values for the detected compressive strength were 5.87% and 3.52%, respectively, and they passed the compressive strength detection of retardation diagrams in complex situations. The excellent performance of this study in real concrete detection meets the demand for concrete compressive detection in reality.

Research on Concrete Compressive Strength Detection Technology Based on Intelligent Machine Vision

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Concrete is the most common and important building material nowadays. Its compressive strength plays a crucial role in the result and safety of the building....

Research on Concrete Compressive Strength Detection Technology Based on Intelligent Machine Vision

Data publikacji: 30 maj 2024

Zakres stron: -

Otrzymano: 30 sty 2024

Przyjęty: 11 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1233

Słowa kluczowe
Machine vision, Edge detection, Feature extraction, Random forest, Compressive strength detection

© 2024 Xianguo Dong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Concrete Compressive Strength Detection Technology Based on Intelligent Machine Vision

Data publikacji: 30 maj 2024

Zakres stron: -

Otrzymano: 30 sty 2024

Przyjęty: 11 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1233

Słowa kluczoweMachine vision, Edge detection, Feature extraction, Random forest, Compressive strength detection

© 2024 Xianguo Dong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Machine vision, Edge detection, Feature extraction, Random forest, Compressive strength detection